Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Abstract

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

“…In the present work, a popular set of statistical metrics (e.g., bias, mean square error, linear correlation coefficient) are employed to assess the model performance since each individual metric has its own strength and weakness [110]. For instance, due to the standardization of observed and forecasted means and variance, the robustness of Pearson's correlation coefficient (r), which exceeds 1 as the perfect model, may have limited meaning [70,95]. Moreover, while root mean square error (RMSE) is relevant for high values, mean absolute error (MAE) assesses all deviations of observed data both in the same manner and regardless of sign [111].…”

“…In terms of the model performance evaluation, despite higher levels of model assessment skill in the error measurement approaches compared with the correlation coefficient (r) which represents the relationship between observed and predicted values [68], it is not totally sensible when applying RMSE and MAE alone [44,69], especially in deep learning method evaluation. Therefore, it is reasonable to apply multiple metrics in model performance evaluation to avoid their specific weaknesses [70]. For this reason, applying multiple evaluation metrics to assess the predictive performance of the LSTM method in near-real-time forecasts is a novelty in this paper.…”

Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

“…Many studies [10] are showing its ability to attain high forecasting precision than its earlier counterpart, or non-DL models. Numerous works have implemented DL in a diverse range of applications such as solar radiation [11,12], pain intensity estimation [13], and seizure diagnosis [14]. In these studies, and the others, DL was commended for its superior capability to handle complex data (e.g., TSP) and approximation through stochastic variables analysis with a nonlinear feature mapping capability.…”

Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings

“…Deep learning is designed to use a neural network structure to represent input and target data. These models use multiple feature extraction layers and learn the complex relationships within the data more efficiently [45]. Recent studies have successfully employed deep learning models in predicting energy efficiency.…”

“…Ryu et al [47] applied deep neural network (DNN)-based load forecasting models and applied them to a demand-side empirical load database. Ghimire et al [45] used the DNN and deep belief network, the two fundamental categories of DL algorithms, coupled with satellite-derived data to predict monthly global solar radiation. Because deep learning models are applicable for predicting time series, DNN was adopted herein to predict hourly solar radiation to effectively determine the amount of power generated by solar cells.…”